Canadian Sport Data revisions

Methods

Pulling and filtering data

• We pulled raw recreational catch data from CREST database: data sources include creel, irec, lodge and log-book data. Data is typically in number of marked and unmarked fish kept and released.

Show the code

#Pull sport data from CREST
source("PullAllSport.R")
# PullAllSport(Start_year) set the calendar year to start querying from to the current year
rslt <- PullAllSport(2005)
quality_report <- rslt[[1]]
estimates <- rslt[[2]]
rm(rslt)

#Load North Coast data from csv.
nbc_aabm<- read.csv("Sport_data_set_NBC_AABM.csv") |>
           as_tibble() |>
           dplyr::select(YEAR, MONTH, AREA, REGION2, MANAGEMENT, SOURCE, MARKS_DESC, TYPE, VAL_calc)|>
           rename(VAL = VAL_calc)|>
           mutate(INCLUDE_15 = 1, VARIANCE = 0)|>
           mutate(season = case_when(
             MONTH %in% c(1:4) ~ "spring",
             MONTH %in% c(5:9) ~ "summer",
             MONTH %in% c(10:12) ~ "fall"))

• Filtered data for marine estimates only

• Filtered the creel data based on high quality creel which meet the following criteria in a given PFMA and month:

  • At least 3 flights for each type of day (weekday or weekend)
  • At least 25 interviews mid week OR at least 10% of interviews from mid-week
  • At least 25 interviews on weekends OR at least 10% of interviews from weekend
  • At least 15 day spread in flights
  • At least 15 day spread in interviews

• After filtering for criteria, we identify where a creel or logbook estimate was made (even if 0).

• We combined estimates under sources called “Lodge Log”,“Lodge Manifest”,“Lodge Manifest - Log”, “Lodge Estimate”, “Log Estimate”, “Lodge eLog” to be called lodge_log.

• We identify a season - spring is months 1:5, summer 6:8 and fall 9:12

• We also expanded the dataset to include all combinations of area, year, month, mark status, kept status and source -> adding NAs where no data was collected

Show the code

#isolating months and areas where there is definitely creel effort, to be used later:
creel_effort<-  estimates |>
                as_tibble() |>
                 mutate(INCLUDE_15 = case_when(
                   SOURCE != "Creel" ~ 1,
                   YEAR == 2006 & MONTH==8& REGION2=="WCVIS"& MANAGEMENT=="ISBM" ~ 1,
                   YEAR == 2005 & MONTH==7& REGION2=="GSS"& MANAGEMENT=="ISBM" ~ 1,
                   TRUE ~ INCLUDE_15
                 ))|>
                filter(INCLUDE_15 ==1, YEAR<2024, SOURCE=="Creel") |>
                dplyr::select(YEAR, MONTH, AREA, REGION2, MANAGEMENT) |>
                unique()
creel_effort$creel_done<-"yes"

#logbook effort
logbook_effort<-  estimates |>
  as_tibble() |>
  mutate(INCLUDE_15 = case_when(
    SOURCE != "Creel" ~ 1,
    YEAR == 2006 & MONTH==8& REGION2=="WCVIS"& MANAGEMENT=="ISBM" ~ 1,
    YEAR == 2005 & MONTH==7& REGION2=="GSS"& MANAGEMENT=="ISBM" ~ 1,
    TRUE ~ INCLUDE_15
  ))|>
  filter(INCLUDE_15 ==1, YEAR<2024, SOURCE %notin% c("Creel", "iREC")) |>
  dplyr::select(YEAR, MONTH, AREA, REGION2, MANAGEMENT) |>
  unique()
logbook_effort$logbook_done<-"yes"

creel_plus_effort<- full_join(creel_effort, logbook_effort) %>%
  mutate(creel_plus_done = case_when(
    logbook_done == "yes" | creel_done == "yes" ~ "yes",
    TRUE ~ "no"
  ))

historic_effort<- nbc_aabm |>
  as_tibble() |>
  dplyr::select(YEAR, MONTH, AREA, REGION2, MANAGEMENT) |>
  unique()
historic_effort$historic_done<-"yes"


Sport_filtered_south_irec_unfiltered<-
  estimates |>
  as_tibble() |>
  filter( YEAR<2024)|>
  filter(AREA %notin% c("Area 29 (In River)", "Campbell River", "Quinsam River", "CR-1", "CR-2", "CR-3", "CR-4", "QR-1", "QR-2", "QR-3", "QR-4")) |>
  mutate(AREA = case_when(
    AREA== "Area 29 (Marine)" ~ "Area 29",
    AREA== "Area 19" & REGION2=="JDF" ~ "Area 19 (JDF)",
    AREA== "Area 19" & REGION2=="GSS" ~ "Area 19 (GS)",
    TRUE ~ as.character(AREA))) |>
  filter(SUB_TYPE == "LEGAL") |>
  mutate(REGION2 = case_when(AREA == "Area 2" ~ "NC", TRUE ~ REGION2)) |>
  mutate(MANAGEMENT = case_when(
        AREA %in% c("Area 2","Area 1", "Area 101", "Area 102",  "Area 142", "Area 2E", "Area 2W") ~ "AABM", TRUE ~ MANAGEMENT )) |>
  mutate(MARKS_DESC = case_when(
    MARKS_DESC == "Not Adipose Checked" ~ "unchecked",
    MARKS_DESC == "Not Checked" ~ "unchecked",
    MARKS_DESC == "Not Applicable" ~ "unchecked",
    MARKS_DESC == "Not Adipose Marked" ~ "unmarked",
    MARKS_DESC == "Adipose Marked" ~ "marked")) |>
  mutate(SOURCE = case_when(
    SOURCE == "Creel" ~ "creel_unfiltered",
    SOURCE == "Historic" ~ "historic",
    SOURCE %in% c("Lodge Log","Lodge Manifest","Lodge Manifest - Log", "Lodge Estimate", "Log Estimate", "Lodge eLog") ~ "lodge_log",
    SOURCE == "iREC" ~ "irec_calibrated",
    TRUE ~ SOURCE )) |>
  group_by(YEAR, MONTH, AREA, REGION2, MANAGEMENT, SOURCE, MARKS_DESC, TYPE, INCLUDE_15) |>
  summarise(VARIANCE=sum(VARIANCE), VAL=sum(ESTIMATE)) |> ungroup()|>
  mutate(season = case_when(
    MONTH %in% c(1:4) ~ "spring",
    MONTH %in% c(5:9) ~ "summer",
    MONTH %in% c(10:12) ~ "fall"
  )) |>
  filter(SOURCE == "creel_unfiltered")


Sport_filtered_south_irec<-
  estimates |>
  as_tibble() |>
  mutate(INCLUDE_15 = case_when(
    SOURCE != "Creel" ~ 1,
    YEAR == 2006 & MONTH==8& REGION2=="WCVIS"& MANAGEMENT=="ISBM" ~ 1,
    YEAR == 2005 & MONTH==7& REGION2=="GSS"& MANAGEMENT=="ISBM" ~ 1,
    TRUE ~ INCLUDE_15
  ))|>
  filter(INCLUDE_15 ==1, YEAR<2024)|>
  filter(AREA %notin% c("Area 29 (In River)", "Campbell River", "Quinsam River", "CR-1", "CR-2", "CR-3", "CR-4", "QR-1", "QR-2", "QR-3", "QR-4")) |>
   mutate(AREA = case_when(
    AREA== "Area 29 (Marine)" ~ "Area 29",
    AREA== "Area 19" & REGION2=="JDF" ~ "Area 19 (JDF)",
    AREA== "Area 19" & REGION2=="GSS" ~ "Area 19 (GS)",
    TRUE ~ as.character(AREA))) |>
  filter(SUB_TYPE == "LEGAL") |>
  mutate(REGION2 = case_when(AREA == "Area 2" ~ "NC", TRUE ~ REGION2)) |>
  mutate(MANAGEMENT = case_when(
          AREA %in% c("Area 2","Area 1", "Area 101", "Area 102",  "Area 142", "Area 2E", "Area 2W") ~ "AABM", TRUE ~ MANAGEMENT )) |>
  mutate(MARKS_DESC = case_when(
    MARKS_DESC == "Not Adipose Checked" ~ "unchecked",
    MARKS_DESC == "Not Checked" ~ "unchecked",
    MARKS_DESC == "Not Applicable" ~ "unchecked",
    MARKS_DESC == "Not Adipose Marked" ~ "unmarked",
    MARKS_DESC == "Adipose Marked" ~ "marked")) |>
  mutate(SOURCE = case_when(
    SOURCE == "Creel" ~ "creel",
    SOURCE == "Historic" ~ "historic",
    SOURCE %in% c("Lodge Log","Lodge Manifest","Lodge Manifest - Log", "Lodge Estimate", "Log Estimate", "Lodge eLog") ~ "lodge_log",
    SOURCE == "iREC" ~ "irec_calibrated",
    TRUE ~ SOURCE )) |>
  group_by(YEAR, MONTH, AREA, REGION2, MANAGEMENT, SOURCE, MARKS_DESC, TYPE, INCLUDE_15) |>
  summarise(VARIANCE=sum(VARIANCE), VAL=sum(ESTIMATE)) |> ungroup()|>
  mutate(season = case_when(
    MONTH %in% c(1:4) ~ "spring",
    MONTH %in% c(5:9) ~ "summer",
    MONTH %in% c(10:12) ~ "fall"
  ))

#Take out NC and CC and add back in from csv but KEEP IREC in
#Add back in NBC data from csv
Sport_filtered_south_irec<-Sport_filtered_south_irec |>
                           rbind(Sport_filtered_south_irec_unfiltered)|>
                           mutate(filter_NC = case_when(
                             REGION2 %in% c("NC", "CC") & SOURCE != "irec_calibrated" ~ "remove",
                             TRUE ~ "keep") ) |>
                           filter(filter_NC=="keep")|>
                           dplyr::select(-filter_NC)|>
                           rbind(nbc_aabm)

Extrapolating unchecked catch

• We also expanded the dataset to include all combinations of area, year, month, mark status, kept status and source -> adding NAs where no data was collected

• Data is typically in number of marked and unmarked fish kept and released, however, there are two other categories when mark status is unknown: unchecked kept and unchecked released. We need to extrapolate to marked and unmarked catch using mark rate

• We calculated mark rate in a number of different ways and prioritize the assignment of mark rate in the following order:

  1. Monthly area calculation - Mark rate calculated for a given source (iREC, creel, or logbook) within a given year, month, and area
  2. Monthly average by source - Mark rate calculated within a given year, month, and area, averaged across sources
  3. Monthly regional average - Mark rate calculated for a given source within a given year, month, and region (averaged across areas within a region)
  4. Seasonal area average - Mark rate calculated for a given source (iREC, creel, or logbook) within a given year, season, and area
  5. Seasonal regional average -Mark rate calculated for a given source within a given year, season, and region
  6. Monthly area average across years - Mark rate calculated for a given source within a given month, area, and across years
  7. Monthly regional average across years- Mark rate calculated for a given source within a given month, region, and across years

Show the code

#2. Mark rate averaged across source
Sport_mark_rate_source<- Sport_filtered_south_irec  %>%
  group_by(YEAR, AREA, MONTH, season, REGION2, MANAGEMENT, SOURCE, MARKS_DESC, TYPE) %>% summarise(sum=sum(VAL)) %>%
  pivot_wider(id_cols = c(YEAR, AREA, MONTH, season, REGION2, SOURCE, MANAGEMENT), names_from=c(MARKS_DESC, TYPE), values_from = sum) |>
  mutate(marked_prop_source = sum(marked_Kept,marked_Released, na.rm = TRUE)/sum(marked_Kept,marked_Released,unmarked_Kept,unmarked_Released, na.rm =TRUE)) %>%
  mutate_all(~ifelse(is.nan(.), NA, .)) %>%
  group_by(YEAR, AREA, MONTH, season, REGION2, MANAGEMENT) %>% summarise(marked_prop_source =mean(marked_prop_source, na.rm=TRUE)) %>%
  dplyr::select(YEAR, AREA, MONTH, season, REGION2, MANAGEMENT, marked_prop_source) %>% ungroup()

#3. Mark rate monthly Regional average
Sport_mark_rate_REGION2<- Sport_filtered_south_irec  %>%
  group_by(YEAR, AREA, MONTH, season, REGION2, MANAGEMENT, SOURCE, MARKS_DESC, TYPE) %>% summarise(sum=sum(VAL)) %>%
  pivot_wider(id_cols = c(YEAR, AREA, MONTH, season, REGION2, MANAGEMENT, SOURCE), names_from=c(MARKS_DESC, TYPE), values_from = sum) %>%
  mutate(marked_prop_REGION2 =sum(marked_Kept,marked_Released, na.rm = TRUE)/sum(marked_Kept,marked_Released,unmarked_Kept,unmarked_Released, na.rm =TRUE)) %>%
  group_by(YEAR, REGION2, MONTH, season, SOURCE, MANAGEMENT) %>% summarise(marked_prop_REGION2 =mean(marked_prop_REGION2, na.rm=TRUE)) %>%
  dplyr::select(YEAR, MONTH, season, REGION2, MANAGEMENT, marked_prop_REGION2) %>% ungroup()

#4. Mark rate seasonal area average
Sport_mark_rate_seasonal<- Sport_filtered_south_irec  %>%
  group_by(YEAR, AREA, MONTH, season, REGION2, MANAGEMENT, SOURCE, MARKS_DESC, TYPE) %>% summarise(sum=sum(VAL)) %>%
  pivot_wider(id_cols = c(YEAR, AREA, MONTH, REGION2, MANAGEMENT, SOURCE, season), names_from=c(MARKS_DESC, TYPE), values_from = sum) %>%
  mutate(marked_prop_seasonal =sum(marked_Kept,marked_Released, na.rm = TRUE)/sum(marked_Kept,marked_Released,unmarked_Kept,unmarked_Released, na.rm =TRUE)) %>%
  group_by(YEAR, season, AREA, REGION2, SOURCE, MANAGEMENT) %>% summarise(marked_prop_seasonal =mean(marked_prop_seasonal, na.rm=TRUE)) %>%
  dplyr::select(YEAR, season, AREA, REGION2, MANAGEMENT, marked_prop_seasonal) %>% ungroup()

#5. Mark rate seasonal regional average
Sport_mark_rate_seasonal_reg<- Sport_filtered_south_irec  %>%
  group_by(YEAR, AREA, MONTH, season, REGION2, MANAGEMENT, SOURCE, MARKS_DESC, TYPE) %>% summarise(sum=sum(VAL)) %>%
  pivot_wider(id_cols = c(YEAR, AREA, MONTH, REGION2, MANAGEMENT, SOURCE, season), names_from=c(MARKS_DESC, TYPE), values_from = sum) %>%
  mutate(marked_prop_seasonal_reg =sum(marked_Kept,marked_Released, na.rm = TRUE)/sum(marked_Kept,marked_Released,unmarked_Kept,unmarked_Released, na.rm =TRUE)) %>%
  group_by(YEAR,season, REGION2, SOURCE, MANAGEMENT) %>% summarise(marked_prop_seasonal_reg =mean(marked_prop_seasonal_reg, na.rm=TRUE)) %>%
  dplyr::select(YEAR, season, REGION2, MANAGEMENT, marked_prop_seasonal_reg) %>% ungroup()

#6. Mark rate area month across years
Sport_mark_rate_area_month<- Sport_filtered_south_irec  %>%
  group_by(YEAR, AREA, MONTH, season, REGION2, MANAGEMENT, SOURCE, MARKS_DESC, TYPE) %>% summarise(sum=sum(VAL)) %>%
  pivot_wider(id_cols = c(YEAR, AREA, MONTH, REGION2, MANAGEMENT, SOURCE, season), names_from=c(MARKS_DESC, TYPE), values_from = sum) %>%
  mutate(marked_prop_area_month =sum(marked_Kept,marked_Released, na.rm = TRUE)/sum(marked_Kept,marked_Released,unmarked_Kept,unmarked_Released, na.rm =TRUE)) %>%
  group_by(AREA, MONTH, season, REGION2, SOURCE, MANAGEMENT) %>% summarise(marked_prop_area_month =mean(marked_prop_area_month, na.rm=TRUE)) %>%
  dplyr::select(AREA, MONTH, season, REGION2, MANAGEMENT, marked_prop_area_month) %>% ungroup()

#7. Mark rate regional month across years
Sport_mark_rate_region_month<- Sport_filtered_south_irec  %>%
  group_by(YEAR, AREA, MONTH, season, REGION2, MANAGEMENT, SOURCE, MARKS_DESC, TYPE) %>% summarise(sum=sum(VAL)) %>%
  pivot_wider(id_cols = c(YEAR, AREA, MONTH, REGION2, MANAGEMENT, SOURCE, season), names_from=c(MARKS_DESC, TYPE), values_from = sum) %>%
  mutate(marked_prop_region_month =sum(marked_Kept,marked_Released, na.rm = TRUE)/sum(marked_Kept,marked_Released,unmarked_Kept,unmarked_Released, na.rm =TRUE)) %>%
  group_by( MONTH, season, REGION2, SOURCE, MANAGEMENT) %>% summarise(marked_prop_region_month =mean(marked_prop_region_month, na.rm=TRUE)) %>%
  dplyr::select(MONTH, season, REGION2, MANAGEMENT, SOURCE, marked_prop_region_month) %>% ungroup()

Show the code

allobs2 <- tidyr::expand(Sport_filtered_south_irec, nesting(AREA, REGION2, MANAGEMENT), YEAR, nesting(MONTH, season), MARKS_DESC, TYPE, SOURCE) %>%
           mutate(bad_combos = case_when(
             AREA %in% c("Area 25", "Area 26", "Area 27") & MANAGEMENT == "ISBM" & MONTH %in% c(1:6,10:12) ~ "bad",
             AREA %in% c("Area 21", "Area 24", "Area 23 (Barkley)", "Area 23 (Alberni Canal") & MANAGEMENT == "ISBM" & MONTH %in% c(1:7,10:12) ~ "bad",
             TRUE ~ "good")) %>%
            filter(bad_combos == "good") %>%
            dplyr::select(-bad_combos)

• We joined the various calculations of mark rate to the catch data and expanded unchecked kept and released into marked and unmarked of those categories using the best available chosen mark rate

Show the code

Sport_mark_rate<- Sport_filtered_south_irec  %>%
  group_by(YEAR, MONTH, season, AREA, REGION2, MANAGEMENT, SOURCE, MARKS_DESC, TYPE) %>% summarise(sum=sum(VAL), sum_VARIANCE=sum(VARIANCE)) %>%
  ungroup() %>%
  full_join(allobs2) %>%
  pivot_wider(id_cols = c(YEAR, MONTH, season, AREA, REGION2, MANAGEMENT, SOURCE), names_from=c(MARKS_DESC, TYPE), values_from = sum) %>%
  group_by(YEAR, MONTH, AREA, season, REGION2, MANAGEMENT, SOURCE) %>%
  mutate(marked_prop = sum(marked_Kept,marked_Released, na.rm = TRUE)/sum(marked_Kept,marked_Released,unmarked_Kept,unmarked_Released, na.rm =TRUE)) %>%
  left_join(Sport_mark_rate_source) %>%
  left_join(Sport_mark_rate_seasonal_reg) %>%
  left_join(Sport_mark_rate_area_month) %>%
  left_join(Sport_mark_rate_REGION2) %>%
  left_join(Sport_mark_rate_seasonal)%>%
  left_join(Sport_mark_rate_region_month) %>%
  mutate_all(~ifelse(is.nan(.), NA, .)) %>%
  mutate(marked_prop_use1 = case_when(
    (is.na(marked_prop) | marked_prop %in% c(0,1)) & !is.na(marked_prop_source) & marked_prop_source %notin% c(0,1) ~ marked_prop_source,
    (is.na(marked_prop) | marked_prop %in% c(0,1)) & (is.na(marked_prop_source)| marked_prop_source %in% c(0,1))  & !is.na(marked_prop_REGION2) & marked_prop_REGION2 %notin% c(0,1) ~ marked_prop_REGION2,
    (is.na(marked_prop) | marked_prop %in% c(0,1)) & (is.na(marked_prop_source)| marked_prop_source %in% c(0,1))  & (is.na(marked_prop_REGION2)| marked_prop_REGION2 %in% c(0,1)) & !is.na(marked_prop_seasonal) & marked_prop_seasonal %notin% c(0,1) ~ marked_prop_seasonal,
    (is.na(marked_prop) | marked_prop %in% c(0,1)) & (is.na(marked_prop_source)| marked_prop_source %in% c(0,1))  & (is.na(marked_prop_REGION2)| marked_prop_REGION2 %in% c(0,1)) & (is.na(marked_prop_seasonal)| marked_prop_seasonal %in% c(0,1)) & !is.na(marked_prop_seasonal_reg) & marked_prop_seasonal_reg %notin% c(0,1) ~ marked_prop_seasonal_reg,
    (is.na(marked_prop) | marked_prop %in% c(0,1)) & (is.na(marked_prop_source)| marked_prop_source %in% c(0,1))  & (is.na(marked_prop_REGION2)| marked_prop_REGION2 %in% c(0,1)) & (is.na(marked_prop_seasonal)| marked_prop_seasonal %in% c(0,1)) & (is.na(marked_prop_seasonal_reg) | marked_prop_seasonal_reg %in% c(0,1)) & !is.na(marked_prop_area_month) & marked_prop_area_month %notin% c(0,1) ~ marked_prop_area_month,
    (is.na(marked_prop) | marked_prop %in% c(0,1)) & (is.na(marked_prop_source)| marked_prop_source %in% c(0,1))  & (is.na(marked_prop_REGION2)| marked_prop_REGION2 %in% c(0,1)) & (is.na(marked_prop_seasonal)| marked_prop_seasonal %in% c(0,1)) & (is.na(marked_prop_seasonal_reg) | marked_prop_seasonal_reg %in% c(0,1)) & (is.na(marked_prop_area_month) |marked_prop_area_month %in% c(0,1))& !is.na(marked_prop_region_month) & marked_prop_region_month %notin% c(0,1) ~ marked_prop_region_month,
    TRUE ~ marked_prop)) %>%
  mutate(marked_prop_use = case_when(
    is.na(marked_prop_use1) ~ 0.5,
    TRUE ~ marked_prop_use1)) %>%
  mutate(marked_Kept_add = marked_prop_use*unchecked_Kept,
         marked_Released_add = marked_prop_use*unchecked_Released,
         unmarked_Kept_add = (1-marked_prop_use)*unchecked_Kept,
         unmarked_Released_add = (1-marked_prop_use)*unchecked_Released) %>%
  mutate(marked_Kept_total = sum(marked_Kept_add, marked_Kept, na.rm = TRUE),
         marked_Released_total = sum(marked_Released_add, marked_Released, na.rm=TRUE),
         unmarked_Kept_total = sum(unmarked_Kept_add, unmarked_Kept, na.rm=TRUE),
         unmarked_Released_total = sum(unmarked_Released_add, unmarked_Released, na.rm=TRUE)) %>%
  ungroup()

Choosing best catch estimate

• We then used criteria to choose the catch estimate by PFMA and month as follows:

  • In months 5-9 use creel+ lodge if that data exists, otherwise use calibrated iREC

  • In months outside of 5-9 use calibrated iREC

• This calculation was grouped ignoring mark and kept status, so that true zeros for one category of catch wouldn’t influence the other categories inclusion.

Show the code

Sport_mark_rate2<-Sport_mark_rate %>%
  dplyr::select(YEAR, MONTH, season, AREA, REGION2, MANAGEMENT, SOURCE, marked_Kept_total, unmarked_Kept_total, marked_Released_total, unmarked_Released_total) %>%
  pivot_longer(cols=c(contains("total")), names_to = "status", values_to = "value") %>%
  group_by(YEAR, MONTH, season, AREA, REGION2, MANAGEMENT, SOURCE) %>%
  summarise_if(is.numeric, sum, na.rm = TRUE) %>%
  pivot_wider(id_cols = c(YEAR, MONTH, season, AREA, REGION2, MANAGEMENT), names_from = SOURCE, values_from = value) %>%
  mutate_all(~ifelse(is.nan(.), NA, .)) %>%
  rowwise() %>%
  group_by(YEAR, MONTH, season, AREA, REGION2, MANAGEMENT) %>%
  mutate(creel_plus = sum(creel,lodge_log, na.rm=TRUE),
         creel_unfiltered_plus = sum(creel_unfiltered,lodge_log, na.rm=TRUE),
         historic_plus = sum(historic,lodge_log, na.rm=TRUE)) %>%
  group_by(YEAR, MONTH, season, AREA, REGION2, MANAGEMENT) %>%
  mutate(catch_estimate_cat = case_when(
    YEAR > 2012 & MONTH %in% c(5:9) & (is.na(creel) | creel==0) ~ "use_irec",
    YEAR > 2012 & MONTH %in% c(1:4,10:12) ~ "use_irec",
    YEAR < 2013 & (is.na(creel_plus) | creel_plus==0) & (!is.na(historic) & historic!=0)~ "use_historic",
    YEAR < 2013 & (is.na(creel_plus) | creel_plus==0) ~ "creel_plus_zero",
    TRUE ~ "use_creel_plus")) %>%
  ungroup() %>% dplyr::select(YEAR, MONTH, season, AREA, REGION2, MANAGEMENT, catch_estimate_cat)



Sport_mark_rate3<-Sport_mark_rate %>%
  dplyr::select(YEAR, MONTH, season, AREA, REGION2, MANAGEMENT, SOURCE, marked_Kept_total, unmarked_Kept_total, marked_Released_total, unmarked_Released_total) %>%
  pivot_longer(cols=c(contains("total")), names_to = "status", values_to = "value") %>%
  pivot_wider(id_cols = c(YEAR, MONTH, season, AREA, REGION2, MANAGEMENT, status), names_from = SOURCE, values_from = value) %>%
  mutate_all(~ifelse(is.nan(.), NA, .)) %>%
  rowwise() %>%
  group_by(YEAR, MONTH, season, AREA, REGION2, MANAGEMENT, status) %>%
  mutate(creel_plus = sum(creel,lodge_log, na.rm=TRUE),
         creel_unfiltered_plus = sum(creel_unfiltered,lodge_log, na.rm=TRUE),
         historic_plus = sum(historic,lodge_log, na.rm=TRUE)) %>%
  group_by(YEAR, MONTH, season, AREA, REGION2, MANAGEMENT) %>%
  left_join(Sport_mark_rate2) %>%
  mutate(catch_estimate = case_when(
    catch_estimate_cat == "use_irec" ~ as.numeric(irec_calibrated),
    catch_estimate_cat == "use_historic" ~  as.numeric(historic_plus),
    catch_estimate_cat == "use_creel_plus" ~ as.numeric(creel_plus),
    catch_estimate_cat == "creel_zero" ~ as.numeric(creel_plus))) %>%
  ungroup() %>%
  relocate(catch_estimate_cat, .after=status)

Splitting fisheries by season

• Assign previous finescale fisheries to the catch data using PFMAs and month

• Assign new finescale fisheries (including terminal) to the catch data using PFMAs and season

• Generally seasonal fisheries fall into the pre-defined categories - spring is months 1:4, summer 5:9 and fall 10:12, with a few exceptions - the WCVI ISBM fisheries are shorter length.

Show the code

Sport_mark_rate_finescale<-
  Sport_mark_rate3%>% ungroup %>%
  mutate(finescale_fishery = case_when(
    AREA%in%c( "Area 125", "Area 126", "Area 127") & MANAGEMENT=="AABM" & season=="fall" ~ "NWCVI S FALL AABM",
    AREA%in%c( "Area 125", "Area 126", "Area 127") & MANAGEMENT=="AABM" & season=="spring" ~ "NWCVI S SPRING AABM",
    AREA%in%c( "Area 125", "Area 126", "Area 127") & MANAGEMENT=="AABM" & season=="summer" ~ "NWCVI S SUMMER AABM",

    AREA%in%c("Area 121", "Area 123", "Area 124") & MANAGEMENT=="AABM" & season=="fall" ~ "SWCVI S FALL AABM",
    AREA%in%c("Area 121", "Area 123", "Area 124") & MANAGEMENT=="AABM" & season=="spring" ~ "SWCVI S SPRING AABM",
    AREA%in%c("Area 121", "Area 123", "Area 124") & MANAGEMENT=="AABM" & season=="summer" ~ "SWCVI S SUMMER AABM",

    AREA%in%c("Area 21", "Area 24", "Area 23 (Barkley)", "Area 23 (Alberni Canal)")  & MONTH%in%c(10:12) ~ "SWCVI S FALL AABM",
    AREA%in%c("Area 21", "Area 24", "Area 23 (Barkley)", "Area 23 (Alberni Canal)")  & season=="spring" ~ "SWCVI S SPRING AABM",
    AREA%in%c("Area 21", "Area 24", "Area 23 (Barkley)", "Area 23 (Alberni Canal)")  & MONTH%in%c(6:7) ~ "SWCVI S SUMMER AABM",

    AREA%in%c("Area 25", "Area 26", "Area 27") & MONTH%in%c(10:12) ~ "NWCVI S FALL AABM",
    AREA%in%c("Area 25", "Area 26", "Area 27") & season=="spring" ~ "NWCVI S SPRING AABM",
    AREA%in%c("Area 25", "Area 26", "Area 27") & MONTH%in%c(6) ~ "NWCVI S SUMMER AABM",

    AREA%in%c("Area 121", "Area 123", "Area 124") & MANAGEMENT=="ISBM" & MONTH%in%c(7:9) ~ "SWCVI S SUMMER ISBM",
    AREA%in%c("Area 21", "Area 24", "Area 23 (Barkley)", "Area 23 (Alberni Canal)") & MONTH%in%c(8,9) ~ "SWCVI S SUMMER ISBM",

    AREA%in%c("Area 125", "Area 126", "Area 127") & MANAGEMENT=="ISBM" & MONTH%in%c(7:9) ~ "NWCVI S SUMMER ISBM",
    AREA%in%c("Area 25", "Area 26", "Area 27") & MONTH%in%c(7,8,9) ~ "NWCVI S SUMMER ISBM",

    (AREA %in%c("Area 13", "Area 14", "Area 15", "Area 16") |REGION2== "GSN")& season=="fall" ~ "NGS S FALL",
    (AREA %in%c("Area 13", "Area 14", "Area 15", "Area 16") |REGION2== "GSN")& season=="spring" ~ "NGS S SPRING",
    (AREA %in%c("Area 13", "Area 14", "Area 15", "Area 16") |REGION2== "GSN")& season=="summer" ~ "NGS S SUMMER",

    (AREA %in%c("Area 17", "Area 18", "Area 19 (GS)", "Area 28", "Area 29") |REGION2== "GSS")& season=="fall" ~ "SGS S FALL", #this captures 19
    (AREA %in%c("Area 17", "Area 18", "Area 19 (GS)", "Area 28", "Area 29") |REGION2== "GSS")& season=="spring" ~ "SGS S SPRING",
    (AREA %in%c("Area 17", "Area 18", "Area 19 (GS)", "Area 28", "Area 29") |REGION2== "GSS")& season=="summer" ~ "SGS S SUMMER",

    (AREA %in% c("Area 11", "Area 111", "Area 12") | REGION2 == "JST") & season=="fall" ~ "JNST S FALL",
    (AREA %in% c("Area 11", "Area 111", "Area 12")| REGION2 == "JST") & season=="spring" ~ "JNST S SPRING",
    (AREA %in% c("Area 11", "Area 111", "Area 12")| REGION2 == "JST") & season=="summer" ~ "JNST S SUMMER",

    AREA %in% c("Area 10", "Area 106", "Area 110", "Area 6", "Area 7", "Area 8", "Area 9", "Area 130", "Area 108", "Area 109", "Area 107") & season=="spring"   ~ "CBC S SPRING",
    AREA %in% c("Area 10", "Area 106", "Area 110", "Area 6", "Area 7", "Area 8", "Area 9", "Area 130", "Area 108", "Area 109", "Area 107") & season=="fall"  ~ "CBC S FALL",
    AREA %in% c("Area 10", "Area 106", "Area 110", "Area 6", "Area 7", "Area 8", "Area 9", "Area 130", "Area 108", "Area 109", "Area 107") & season=="summer"   ~ "CBC S SUMMER",

    AREA %in% c("Area 2","Area 1", "Area 101", "Area 102",  "Area 142", "Area 2E", "Area 2W")& season=="spring"   ~ "NBC AABM S SPRING",
    AREA %in% c("Area 2","Area 1", "Area 101", "Area 102",  "Area 142", "Area 2E", "Area 2W")& season=="fall"  ~ "NBC AABM S FALL",
    AREA %in% c("Area 2","Area 1", "Area 101", "Area 102",  "Area 142", "Area 2E", "Area 2W")& season=="summer"  ~ "NBC AABM S SUMMER",

    AREA %in% c( "Area 103", "Area 104", "Area 105", "Area 3", "Area 4", "Area 5")& season=="spring"  ~ "NBC ISBM S SPRING",
    AREA %in% c( "Area 103", "Area 104", "Area 105", "Area 3", "Area 4", "Area 5")& season=="fall"  ~ "NBC ISBM S FALL",
    AREA %in% c( "Area 103", "Area 104", "Area 105", "Area 3", "Area 4", "Area 5")& season=="summer"  ~ "NBC ISBM S SUMMER",

    (AREA %in% c( "Area 19 (JDF)", "Area 20", "Area 20 (East)", "Area 20 (West)") | REGION2 == "JDF") & season=="spring"  ~ "CA JDF S SPRING", #this captures 19
    (AREA %in% c( "Area 19 (JDF)", "Area 20", "Area 20 (East)", "Area 20 (West)") | REGION2 == "JDF") & season=="fall"  ~ "CA JDF S FALL",
    (AREA %in% c( "Area 19 (JDF)", "Area 20", "Area 20 (East)", "Area 20 (West)") | REGION2 == "JDF") & season=="summer"  ~ "CA JDF S SUMMER")) %>%


  mutate(finescale_fishery_old = case_when(
    AREA%in%c( "Area 125", "Area 126", "Area 127") & MANAGEMENT=="AABM" & MONTH%in%c(1:12) ~ "NWCVI S AABM",
    AREA%in%c("Area 121", "Area 123", "Area 124") & MANAGEMENT=="AABM" & MONTH%in%c(1:12) ~ "SWCVI S AABM",
    AREA%in%c("Area 21", "Area 24", "Area 23 (Barkley)", "Area 23 (Alberni Canal)")  & MONTH%in%c(1:7, 10:12) ~ "SWCVI S AABM",
    AREA%in%c("Area 25", "Area 26", "Area 27") & MONTH%in%c(1:6, 10:12) ~ "NWCVI S AABM",
    AREA%in%c("Area 121", "Area 123", "Area 124") & MANAGEMENT=="ISBM" & MONTH%in%c(7:12) ~ "SWCVI S ISBM",
    AREA%in%c("Area 125", "Area 126", "Area 127") & MANAGEMENT=="ISBM" & MONTH%in%c(7:12) ~ "NWCVI S ISBM",
    AREA%in%c("Area 21", "Area 24", "Area 23 (Barkley)", "Area 23 (Alberni Canal)") & MONTH%in%c(8,9) ~ "SWCVI S ISBM",
    AREA%in%c("Area 25", "Area 26", "Area 27") & MONTH%in%c(7,8,9) ~ "NWCVI S ISBM",
    (AREA %in%c("Area 13", "Area 14", "Area 15", "Area 16") |REGION2== "GSN")& MONTH%in%c(1:12) ~ "NGS S",
    (AREA %in%c("Area 17", "Area 18",  "Area 19 (GS)", "Area 28", "Area 29") |REGION2== "GSS")& MONTH%in%c(1:12) ~ "SGS S",
    (AREA %in% c("Area 11", "Area 111", "Area 12") | REGION2 == "JST") & MONTH%in%c(1:12) ~ "JNST S",
    AREA %in% c("Area 10", "Area 106", "Area 110", "Area 6", "Area 7", "Area 8", "Area 9", "Area 130", "Area 108", "Area 109", "Area 107")& MONTH%in%c(1:12)  ~ "CBC S",
    AREA %in% c("Area 2","Area 1", "Area 101", "Area 102",  "Area 142", "Area 2E", "Area 2W")& MONTH%in%c(1:12)  ~ "NBC AABM S",
    AREA %in% c( "Area 103", "Area 104", "Area 105", "Area 3", "Area 4", "Area 5")& MONTH%in%c(1:12)  ~ "NBC ISBM S",
    (AREA %in% c( "Area 19 (JDF)", "Area 20", "Area 20 (East)", "Area 20 (West)") | REGION2 == "JDF") & MONTH%in%c(1:12) ~ "CA JDF S"))

• We also identified a fishery specific consistently creel or logbooked coverage period for the summer period

  • This is where there is consistent data throughout the time series (from 2005 to 2023) from the creel + logbook data

  • This consistent chunk of data will be used to predict the estimated catch in the section of the time series where we don’t have iREC

  • The summer coverage period included all months within 6:8 that are consistent through the timeseries.

Show the code

Sport_mark_rate_finescale<- Sport_mark_rate_finescale %>% ungroup %>%
  mutate(summer_coverage_tf = case_when(
    finescale_fishery_old == "CA JDF S" & MONTH %in% c(7:8)  ~ "yes",
    finescale_fishery_old == "JNST S" & MONTH %in% c(7:8)  ~ "yes",
    finescale_fishery_old == "NGS S" & MONTH %in% c(7:8)  ~ "yes",
    finescale_fishery_old == "SGS S" & MONTH %in% c(7:8) & YEAR %notin% c(2016)  ~ "yes",
    finescale_fishery_old == "SWCVI S ISBM" & MONTH %in% c(8) ~ "yes",
    finescale_fishery_old == "SWCVI S AABM" & MONTH %in% c(7:8)  ~ "yes",
    finescale_fishery_old == "SWCVI S" & MONTH %in% c(7:8) ~ "yes",
    finescale_fishery_old == "NWCVI S ISBM" & MONTH %in% c(7:8) ~ "yes",
    finescale_fishery_old == "NWCVI S AABM" & MONTH %in% c(7:8)  ~ "yes",
    finescale_fishery_old == "NWCVI S" & MONTH %in% c(7:8) ~ "yes",
    finescale_fishery_old == "CBC S" & MONTH %in% c(7:8) ~ "yes",
    finescale_fishery_old == "NBC AABM S" & MONTH %in% c(7:8) ~ "yes",
    finescale_fishery_old == "NBC ISBM S" & MONTH %in% c(7:8) ~ "yes",
    .default = "no"))

• We sum up catch by mark and kept status for each year and new finescale fishery of the defined coverage periods for creel and for the previously calculated catch estimate. This is the data used in the modelling exercise described below.
• We also calculate creel effort - a proportion of months and areas in a given finescale fishery that we surveyed using creel or logbooks. This is used in the modelling efforts below.

Show the code

Sport_mark_rate_finescale_sum<- Sport_mark_rate_finescale %>%
  filter(!is.na(finescale_fishery)) %>%
  group_by(YEAR, status, finescale_fishery_old, finescale_fishery) %>%
  summarise_at(vars(creel:catch_estimate), sum, na.rm=TRUE)

Sport_creel_finescale_summer<- Sport_mark_rate_finescale %>%
  filter(!is.na(finescale_fishery_old), summer_coverage_tf=="yes") %>%
  group_by(YEAR, status, finescale_fishery_old) %>%
  summarise_at(vars(creel_plus), sum, na.rm=TRUE) %>%
  rename(creel_plus_summer=creel_plus)

Sport_historic_finescale_summer<- Sport_mark_rate_finescale %>%
  filter(!is.na(finescale_fishery_old), summer_coverage_tf=="yes") %>%
  group_by(YEAR, status, finescale_fishery_old) %>%
  summarise_at(vars(historic), sum, na.rm=TRUE) %>%
  rename(historic_summer=historic)

Sport_creel_finescale_creel_effort<- Sport_mark_rate_finescale %>%
  full_join(creel_plus_effort) %>%
  mutate(creel_plus_done = case_when(
    is.na(creel_plus_done)~ "no",
    TRUE ~ creel_plus_done)) %>%
  filter(!is.na(finescale_fishery_old), summer_coverage_tf=="yes") %>%
  group_by(YEAR, finescale_fishery_old, finescale_fishery) %>% count(creel_plus_done) %>%
  pivot_wider(names_from = creel_plus_done, values_from = n) %>%
  mutate(creel_effort = sum(yes, na.rm=TRUE)/sum(no, yes, na.rm=TRUE)) %>%
  ungroup() %>%
  dplyr::select(YEAR, finescale_fishery_old,creel_effort)

Sport_creel_finescale_historic_effort<- Sport_mark_rate_finescale %>%
  full_join(historic_effort) %>%
  mutate(historic_done = case_when(
    is.na(historic_done)~ "no",
    TRUE ~ historic_done)) %>%
  filter(!is.na(finescale_fishery_old), summer_coverage_tf=="yes") %>%
  group_by(YEAR, finescale_fishery_old, finescale_fishery) %>% count(historic_done) %>%
  pivot_wider(names_from = historic_done, values_from = n) %>%
  mutate(historic_effort = sum(yes, na.rm=TRUE)/sum(no, yes, na.rm=TRUE)) %>%
  ungroup() %>%
  dplyr::select(YEAR, finescale_fishery_old,historic_effort)


#Year, finescale fishery
Sport_mark_rate_finescale_combined<-left_join(Sport_mark_rate_finescale_sum, Sport_creel_finescale_summer)
Sport_mark_rate_finescale_combined<-left_join(Sport_mark_rate_finescale_combined, Sport_historic_finescale_summer)
Sport_mark_rate_finescale_combined<-left_join(Sport_mark_rate_finescale_combined, Sport_creel_finescale_creel_effort)
Sport_mark_rate_finescale_combined<-left_join(Sport_mark_rate_finescale_combined, Sport_creel_finescale_historic_effort)

Sport_mark_rate_finescale_combined<-Sport_mark_rate_finescale_combined %>% mutate(mark_status = case_when(
  status %in% c("marked_Kept_total", "marked_Released_total") ~ "marked",
  TRUE ~ "unmarked"
))%>% mutate(kept_status = case_when(
  status %in% c("marked_Kept_total", "unmarked_Kept_total") ~ "Kept",
  TRUE ~ "Released"
))

Sport_mark_rate_finescale_combined<-Sport_mark_rate_finescale_combined %>% mutate(season = case_when(
  str_detect(finescale_fishery, "SUMMER")  ~ "summer",
  str_detect(finescale_fishery, "SPRING")  ~ "spring",
  str_detect(finescale_fishery, "FALL")  ~ "fall"))

Visualization

• Alternatively, we can add up the catch to get total catch estimates across fisheries by month.

Show the code

yearMonth <- plyr::ddply(Sport_mark_rate_finescale, c( "YEAR", "MONTH", "finescale_fishery_old"), summarise,
                         sum_creel = sum(creel, na.rm = TRUE), sum= sum(creel_plus, na.rm = TRUE), sum_historic= sum(historic_plus, na.rm = TRUE), sum_catch_estimate = sum(catch_estimate, na.rm = TRUE), sum_irec = sum(irec_calibrated, na.rm = TRUE)) %>% filter(finescale_fishery_old!="NA") %>% mutate(across(where(is.numeric), ~na_if(., 0))) 


yearMonth_catch_estimate_1<-yearMonth %>% filter(sum_catch_estimate!=sum,sum_catch_estimate!=sum_historic, sum_catch_estimate!=sum_irec)

yearMonth_irec_1<-yearMonth %>% filter(sum_catch_estimate==sum_irec)

• We can visualize this as creel and logbook only information and look for where there are gaps in coverage

• We can then add on irec data to see where irec adds information

• We see that almost always a creel- irec combination (blue) or irec (red) on its own will be chosen as the best estimate for the whole month. This is because there are typically a few PFMAs in the larger fishery that are not creeled in each month.

Modelling

• Now we take the chunks of summer, spring, fall consistently creeled or logbooked data between 2013 and 2023 and establish a relationship via a model with the irec based catch estimate.

• We investigate various models using a systematic approach:

  • One model for all pre-terminal sport fisheries

  • Same dataset tested for all models - therefore done without NAs

  • We didn’t consider random effects models since we don’t have enough levels of each factor to warrant random effects inclusion (need >10 typically)

  • For all candidate models we used the DHARMa package to create a qq-plot to detect deviations in observations from expected distribution and a plot of the residuals against predicted values to detect patterns in residuals. We visually assessed whether candidate models improved plots.

  • We also compared candidate models using AIC, a lower AIC indicating a better model fit.

  • The order of comparisons was as follows:

    • First we compared three distributions: normal (gaussian), poisson, and Gamma distribution to see which fit our data the best on the full model. These distributions are all consistent with our positive, continuous data

      \[ catch = creel.summer * status * finescale.fishery * season * creel.effort \]

    • Next, using the chosen distribution, we compared the full model with all terms to models with each of the terms dropped sequentially except for creel.summer which was our main interest. We did this first by dropping out full terms from the full interaction model, then used the dredge function in MuMIn to sequentially drop out the various sub-interactions

    • We selected the model with the lowest AIC and best visual diagnostics.

Pre-terminal fisheries

• The best model was one with a gamma distribution:

                          dAIC   df 
Season_model_full_gamma      0.0 297
Season_model_full         2171.3 297
Season_model_full_poisson    Inf 296

• Next, we investigated if the fully interactive model is better than a subset of interactions. We used the dredge function from the MuMin package to sequentially drop terms (including all interactions) and chose to include all terms in models with AIC <2.

• Therefore the best model included summer creel, marked and kept status, finescale fishery, creel effort, and season and various interactions of these terms

  \[ catch = creel.summer+finescale.fishery+season+status+ creel.summer:finescale.fishery + creel.summer:season + creel.summer:status+ finescale.fishery:season + finescale.fishery:status + season:status \]

Show the code

#Adding predicted data to the modelled set
Season_south_old<- Sport_mark_rate_finescale_combined %>% filter(YEAR %in% c(2005:2012)) %>% ungroup() %>% mutate(pred_cat = "predicted") %>% filter(!str_detect(finescale_fishery, "CBC|NBC"))
Season_south_old_new<-predict.glm(Season_model_gamma_full_spec, newdata =  Season_south_old, type = "response")
Season_south_old_new_2<-Season_south_old %>%   mutate(catch_estimate_predicted = Season_south_old_new)

Season_south2<-Season_south %>% mutate(catch_estimate_predicted = catch_estimate, pred_cat= "observed")
Season_south_combined<- rbind(Season_south_old_new_2, Season_south2)



#### Adding confidence intervals based on model to a dataframe for plotting purposes
#based on this blog: https://fromthebottomoftheheap.net/2018/12/10/confidence-intervals-for-glms/

family.set <- family(Season_model_gamma_full_spec)
ilink.family.set<- family.set$linkinv

want_marked_kept <- seq(1, nrow(Season_south_no_nas %>% filter(status == "marked_Kept_total")), length.out = 1000)
want_marked_released <- seq(1, nrow(Season_south_no_nas %>% filter(status == "marked_Released_total")), length.out = 1000)
want_umarked_kept <- seq(1, nrow(Season_south_no_nas %>% filter(status == "unmarked_Kept_total")), length.out = 1000)
want_umarked_released <- seq(1, nrow(Season_south_no_nas %>% filter(status == "unmarked_Released_total")), length.out = 1000)

mod<-Season_model_gamma_full_spec

ndata_marked_kept <- with(Season_south_no_nas %>% filter(status == "marked_Kept_total"), data_frame(creel_plus_summer= seq(min(creel_plus_summer), max(creel_plus_summer), length = 1000),  status = "marked_Kept_total", season = season[want_marked_kept], finescale_fishery_old=finescale_fishery_old[want_marked_kept], creel_effort=creel_effort[want_marked_kept], finescale_fishery=finescale_fishery[want_marked_kept]))

ndata_marked_released <- with(Season_south_no_nas %>% filter(status == "marked_Released_total"), data_frame(creel_plus_summer= seq(min(creel_plus_summer), max(creel_plus_summer), length = 1000),  status = "marked_Released_total", season = season[want_marked_released], finescale_fishery_old=finescale_fishery_old[want_marked_released], creel_effort=creel_effort[want_marked_released], finescale_fishery=finescale_fishery[want_marked_released]))

ndata_unmarked_kept <- with(Season_south_no_nas %>% filter(status == "unmarked_Kept_total"), data_frame(creel_plus_summer= seq(min(creel_plus_summer), max(creel_plus_summer),length = 1000),  status = "unmarked_Kept_total", season = season[want_umarked_kept], finescale_fishery_old=finescale_fishery_old[want_umarked_kept], creel_effort=creel_effort[want_umarked_kept], finescale_fishery=finescale_fishery[want_umarked_kept]))


ndata_unmarked_released <- with(Season_south_no_nas %>% filter(status == "unmarked_Released_total"), data_frame(creel_plus_summer= seq(min(creel_plus_summer), max(creel_plus_summer), length = 1000),  status =  "unmarked_Released_total", season = season[want_umarked_released], finescale_fishery_old=finescale_fishery_old[want_umarked_released], creel_effort=creel_effort[want_umarked_released], finescale_fishery=finescale_fishery[want_umarked_released]))

ndata<- bind_rows(ndata_marked_kept,ndata_marked_released, ndata_unmarked_kept, ndata_unmarked_released)

## add the fitted values by predicting from the model for the new data
ndata<- add_column(ndata, fit = predict(mod, newdata = ndata, type = 'response'))
ndata<- bind_cols(ndata, setNames(as_tibble(predict(mod, ndata, se.fit = TRUE)[1:2]),
                                                   c('fit_link','se_link')))

ndata <- mutate(ndata,
                fit_resp  = ilink.family.set(fit_link),
                right_upr = ilink.family.set(fit_link + (2 * se_link)),
                right_lwr = ilink.family.set(fit_link - (2 * se_link)))

• summarizing

Show the code

yearMonth2_season <- plyr::ddply(Sport_mark_rate_finescale, c( "YEAR", "MONTH", "finescale_fishery"), summarise,
                         sum_creel = sum(creel, na.rm = TRUE), sum= sum(creel_plus, na.rm = TRUE), sum_historic= sum(historic_plus, na.rm = TRUE), sum_catch_estimate = sum(catch_estimate, na.rm = TRUE), sum_irec = sum(irec_calibrated, na.rm = TRUE)) %>% filter(finescale_fishery!="NA") %>% mutate(across(where(is.numeric), ~na_if(., 0))) 


yearMonth_catch_estimate_2_season<-yearMonth2_season %>% filter(sum_catch_estimate!=sum,sum_catch_estimate!=sum_historic, sum_catch_estimate!=sum_irec)

yearMonth_irec_2_season<-yearMonth2_season %>% filter(sum_catch_estimate==sum_irec)

• Produced seasonal plots

CA JDF S FALL

CA JDF S SPRING

CA JDF S SUMMER

JNST S FALL

JNST S SPRING

JNST S SUMMER

NGS S FALL

NGS S SPRING

NGS S SUMMER

NWCVI S FALL AABM

NWCVI S SPRING AABM

NWCVI S SUMMER AABM

NWCVI S SUMMER ISBM

SGS S FALL

SGS S SPRING

SGS S SUMMER

SWCVI S FALL AABM

SWCVI S SPRING AABM

SWCVI S SUMMER AABM

SWCVI S SUMMER ISBM

• forloop for old finescale fisheries

CA JDF S

JNST S

NGS S

NWCVI S AABM

NWCVI S ISBM

SGS S

SWCVI S AABM

SWCVI S ISBM

NBC and CBC

Methods

Northern and Central BC data is collected in different ways than South Coast. Data was in various formats and pieced together as follows:

Northern BC - NBC AABM S

• Comprises Areas 1, 2 (East and West), 101, 102, 142

• Data for 2005 to 2008 for areas 1 and 2W found in the Fixed Sport Estimates had total unchecked (for mark status) kept and released #s of fish in the sheet QCI Sport. The sheet “ALL” has an estimate of Mark rate. Therefore:

  • Marked Kept = Unchecked Kept x mark rate

  • Marked Released = Unchecked Released x mark rate

  • Unmarked Kept = Unchecked Kept x (1- mark rate)

  • Unmarked Released = Unchecked Released x (1- mark rate)

• From 2009-2019, for Area 1, 2W, (and beginning in 2017 for 2E) there was only Unchecked Kept catch available and mark rate. Therefore we used the release rate from the Haida creel for the whole year for all of NBC AABM S to get releases.

  • Marked Kept = Unchecked Kept x mark rate

  • Marked Released = (Unchecked Kept x Release Ratio) x mark rate

  • Unmarked Kept = Unchecked Kept x (1- mark rate)

  • Unmarked Released = (Unchecked Kept x Release Ratio) x (1- mark rate)

• From 2020 to present, for Area 1, 2W, and 2E there was only Unchecked Kept and Released catch available by month and mark rate. We used mark rate from iREC to extrapolate to marked and unmarked as above (2005-2008 period).

• We modelled NBC AABM separately since the creel+ logbook data is collected in a unique way and modelled against calibrated iREC-only estimates.

• The best model included historic effort, mark status and season

  \(catch = historic summer + season + mark status + historic effort * historic summer + historic effort*mark status + historic summer* mark status + historic summer*season + mark status*season\)

Show the code

Season_north_aabm<-Sport_mark_rate_finescale_combined%>% filter(YEAR %in% c(2013:2023)) %>% filter(finescale_fishery_old == "NBC AABM S")

#Modelling comparisons need to be done on models with same # of NAs - so drop nas
Season_north_aabm_no_nas<-Season_north_aabm %>% drop_na(any_of(c("historic_summer", "mark_status", "finescale_fishery_old", "season", "historic_effort", "kept_status")))




North_aabm_model_gamma_drop_kept_spec<- glm(formula = catch_estimate+1 ~historic_summer+season+mark_status+historic_effort*historic_summer +
                                              historic_effort*mark_status + historic_summer*mark_status + historic_summer*season + mark_status*season,  family=Gamma(link = "log"), data = Season_north_aabm_no_nas)

#res_gam_drop_kept_spec <- simulateResiduals(North_aabm_model_gamma_drop_kept_spec, plot = T, quantreg=T)
#summary(North_aabm_model_gamma_drop_kept_spec)

#Adding predicted data
Season_north_aabm_old<- Sport_mark_rate_finescale_combined %>% filter(YEAR %in% c(2005:2012)) %>% ungroup() %>% mutate(pred_cat = "predicted") %>% filter(finescale_fishery_old == "NBC AABM S")
Season_north_aabm_old_new<-predict.glm(North_aabm_model_gamma_drop_kept_spec, newdata =  Season_north_aabm_old, type = "response")
Season_north_aabm_old_new_2<-Season_north_aabm_old %>%   mutate(catch_estimate_predicted = Season_north_aabm_old_new)

Season_north_aabm2<-Season_north_aabm %>% mutate(catch_estimate_predicted = catch_estimate, pred_cat= "observed")

Season_north_aabm_combined<- rbind(Season_north_aabm_old_new_2, Season_north_aabm2)

• forloop

NBC AABM S FALL

NBC AABM S SPRING

NBC AABM S SUMMER

Central BC - CBC S

• Good coverage 6:8 only, maybe 9 if omit a couple years

• irec bumps this out by a few months but not all year

Northern BC - NBC ISBM

• Missing a lot of data both before and after 2012.
• Data appears to be 5:8 only, irec expands to all year